Data Wrangling in the Tidyverse

• What is tidy data and the {tidyverse}?
• Solving data manipulation challenges with {dplyr}
• Dates and times with the {lubridate} package
• Creating tidy data with {tidyr}
• Dealing with categorical variables using {forcats}

Session 1:

By the end of session 1 participants will …

• be familiar with the {dplyr} data wrangling package, and be able to use its main functions.
• be able to combine data based on specific columns using {dplyr} join functions.
• be able to manipulate dates and times in R using {lubridate}.

Session 2:

By the end of session 2 participants will …

• be able to transform their data between long and wide data formats using {tidyr}.
• be able to join and separate columns of a dataset.
• be familiar with different methods of dealing with both explicit and implicit missing values.
• understand how to apply the {forcats} package to create and re-level factors in datasets, and also reorder variables in a plot.

This course does not include:

• Content from the {stringr} package, which helps with splitting and combining strings, manipulating text data and working with regular expressions. We have a Text Mining in R course which covers {stringr} in detail.

• The {purrr} package. If you want to learn more about {purrr} see our Functional Programming with {purrr} course.

• Although {ggplot2} features in this course, we recommend attending our Data Visualisation with ggplot2 course if you want to learn more about creating data visualisations in R.

This course assumes familiarity with the concepts taught in our Introduction to R course. In particular, we build on the underlying {tidyverse} theory by introducing new {tidyverse} packages. If you have limited or no experience in R, we would advise you to complete our Introduction to R course, before attending.